Convolution neural network for identification of obstructive sleep apnea

Serein Al-Ratrout, Abdulnasir Hossen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Identification of patients with obstructive sleep apnea from normal subjects is essential for most of hospitals. Artificial intelligence techniques are encouraged for simplicity and being less costly and also for more accurate performances compared to traditional identification methods in hospitals A convolutional neural network is used in this work for feature matching process, while the continuous wavelet transform is used for feature extraction. 40 obstructive sleep apnea subjects plus 20 normal subjects RRI data are used in this work. The data is obtained from the MIT databases. The data is divided into 80% for training and 20% for validation. A compromise between the data size and the efficiency of identification is studied. The data is divided into different lengths segments for this purposes. The results are shown in terms of subject identification and also in terms of segment identification. Voting process is included to identify subjects based on segments identification results. The best subject identification result obtained is 93.8% for trial group and 83.3% for validation group. The best segment identification result obtained is 88.45 for trial group and 82.5% for validation group.

Original languageEnglish
Title of host publicationTIPTEKNO 2022 - Medical Technologies Congress, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665454322
Publication statusPublished - 2022
Event2022 Medical Technologies Congress, TIPTEKNO 2022 - Antalya, Turkey
Duration: Oct 31 2022Nov 2 2022

Publication series

NameTIPTEKNO 2022 - Medical Technologies Congress, Proceedings


Conference2022 Medical Technologies Congress, TIPTEKNO 2022


  • continuous wavelet transform
  • convolution neural network
  • identification
  • sleep apnea
  • voting

ASJC Scopus subject areas

  • Computer Science Applications
  • Biomedical Engineering
  • Electronic, Optical and Magnetic Materials
  • Medicine (miscellaneous)
  • Health Informatics
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

Cite this